What Is a Digital Twin?

A digital twin is a virtual replica of a physical object that is designed to change alongside the object it represents. Using real-time data gathered from sensors, a digital twin accurately reflects how an object or system behaves throughout its life cycle, which is useful for analyzing historical data and testing possible scenarios.

Digital twin technology has major implications for companies across numerous sectors, enabling teams to predict machine performance, monitor equipment, devise efficient workflows, inform business decisions and much more.

 

What Is a Digital Twin?

A digital twin is a virtual copy of a physical object. Depending on the context, an object can be an individual part like a screw, a smaller system like a robotic arm, a process like an assembly line workflow or a larger environment like an entire country. Relying on sensors attached to the object, a digital twin collects real-time data to accurately reproduce the object’s behavior. This data is used to simulate how an object would behave under various circumstances.

Digital twins are dynamic, meaning they evolve alongside the objects they represent, continuously storing real-time data while preserving historical data. This lets you view an object at different points in time.

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Types of Digital Twins

Digital twins serve many purposes, but they can be organized according to four main types.   

Component Twins

Also known as parts twins, component twins reproduce the smallest unit in a system. Within a factory, a component twin could be the digital representation of a screw. This allows manufacturing teams to focus on how a single part handles particular stressors and variables. 

Asset Twins

An asset refers to two or more components that interact with each other within a system, such as two gears within a conveyor belt. Asset twins then replicate two or more parts and provide performance data that can be used to understand how these parts work together. 

System Twins

System twins treat a product or system as a single unit — such as a robotic arm in a factory — to show how various components work together as a broader whole. This provides a bird’s-eye view of a system, making it easier to identify areas of improvement.  

Process Twins 

Process twins portray different systems as a single unit, showing how these systems work together and make adjustments. A process twin could depict operations within a factory and help teams decide how to optimize workflows.

 

How Are Digital Twins Used?

Digital twins are still an emerging technology, but organizations across many industries have found creative ways to use it.  

Manufacturing

Manufacturers use factory digital twins to provide more visibility into their everyday operations. This makes it easier to identify obstacles, reorganize schedules to increase workers’ efficiency and inform decisions with concrete data. With a deeper understanding of what’s happening on the floor, manufacturers can also more accurately forecast production and bottlenecks.   

Construction

Digital twins of construction sites enable teams to more closely assess potential risks, decide how to execute projects and keep track of real-time progress. This can all be done without having to visit the site in person — a kind of “super power” granted by digital twins, according to Bryan Landsiedel, currently the head of ML fleet operations and previously the head of digital twins at Google.

“Remote workers can be instantly ‘teleported’ to a remote physical location to review the status of an issue, monitor ongoing operations or coordinate onsite activities from a centralized location,” Landsiedel told Built In.

Healthcare

Through digital twins, doctors can collect data on individuals instead of referencing data on entire demographic groups, resulting in more personalized care. For example, healthcare professionals use digital twins of tumors to understand how individual cancer patients may react to certain drugs, allowing them to tailor treatments to each patient.

Logistics

Companies can design digital twins of entire environments to simulate scenarios involving a number of factors, including weather events, human-caused disruptions and fuel prices. By being able to survey their options, organizations can better navigate supply chain challenges and adapt quickly when unexpected problems arise.

Smart Cities

The Georgia cities of Columbus and Warner Robins use digital twin technology to address traffic build-up and study patterns of criminal activity. Meanwhile, the University of Florida built a digital twin of Jacksonville, so the city can construct more sustainable buildings based on simulations and prepare for a changing climate.

Government

Singapore designed a digital twin of the country to anticipate and understand climate risks, so it can better manage its resources and apply renewable energy solutions. The country of Tuvalu has considered digital twinning too — as a way to preserve its government and cultural customs as rising waters threaten to submerge the island nation.

Aerospace

UC Santa Cruz professor Ricardo Sanfelice is exploring how digital twins of spacecraft could be used to test how spacecraft would respond to different scenarios and inform the design of the spacecraft, saving time and costs. The goal is to build spacecraft that can carry out complicated missions like cleaning up space junk around Earth’s atmosphere.

Energy

Energy providers rely on digital twins of hydrogen plants to determine whether plants are economically viable and secure funding in the process. They can also reference simulations to optimize the design of hydrogen plants. In addition, national laboratories view digital twins as an avenue for compiling data on hydropower systems to monitor real-time performance and make changes to refine operations as needed.

Automotive

Thanks to a partnership with Synopsis, automotive company Continental is using digital twins to simulate the performance of car parts and determine their compatibility with different car models. This proactive approach allows automotive manufacturers to fine-tune parts before implementing them, reducing costs and speeding up the development process.

Retail 

Home-improvement retailer Lowe’s teamed up with chipmaker NVIDIA to design digital twins of two of its stores, allowing store associates to better manage inventory, arrange items based on customer behavior and note store-design enhancements. Meanwhile, online marketplace eBay used digital twins to develop 3D models of items with which online shoppers could interact.

 

Advantages of Digital Twins

Digital twins promise to transform businesses with lasting benefits. 

Provides Real-Time Data on Equipment and Processes

Workplaces like industrial facilities can use digital twins to collect real-time data on equipment and processes. Teams can then identify and refine messy workflows, anticipate maintenance and study the behavior of machines before implementing them. These proactive measures help companies cut down on costly disruptions and reduce unnecessary waste, contributing to a more efficient workplace.

Delivers Insights on Future Product Performance

Running tests with digital twins provides valuable insights into how products will perform under different conditions and environments. Even after machines are used, digital twins allow teams to revisit historical data when an issue does occur to discover its cause. “This can help workers better understand the sequence of events that led to an unexpected event,” Landsiedel said.

Informs Decision-Making With 3D Models

The ability of digital twins to deliver detailed, dynamic 3D models of entire systems gives companies a holistic and accurate view of various processes. Leaders can use this when deciding which workflows to automate, how to cut down on expenses, when to upgrade machinery and how to address other business challenges.

Reduces Onsite Work and Potential Accidents 

Digital twins can continuously gather data on an object’s performance without requiring someone to interact with them in person at a worksite. This limits the chances of workplace accidents occurring. 

Offers a Common Space for Digital Collaboration

With a single source for viewing updated data on an object, members of different departments can better visualize how a product functions.

“Within digital twins, engineers from various disciplines can collaborate seamlessly and iterate in virtual spaces on product designs,” Raphael Schor, the principal of manufacturing and mobility at MongoDB, told Built In. “That accelerates the design phase while maintaining the precision required for manufacturing excellence.”

Contributes to More Cost-Efficient Workflows

Automating workflows, addressing problems before they occur and lessening the chances of workplace injuries all help cut down on costs. These perks can also contribute to higher productivity among machines and employees, yielding greater returns for businesses that integrate digital twins into their operations.   

 

Disadvantages of Digital Twins

While digital twins hold plenty of potential, there are still complications that companies need to consider before adopting this technology.  

Cyber Attacks

Digital twins offer a direct connection to the physical objects they represent, making them clear targets of cyber attackers. If hackers are able to infiltrate a digital twin, they not only have access to information on a product but can also manipulate the product by tinkering with its digital twin. 

Data Privacy Violations 

Even applied with the best of intentions, digital twins may stoke concerns around data privacy. Take the example of creating a personalized digital twin of a heart, which would require highly sensitive health data to be collected. If patients don’t understand the implications of developing a digital twin or aren’t comfortable being monitored for data collection, a use case like this could result in data privacy violations.

Data Inaccuracies

If it uses flawed data, a digital twin could lead to inaccurate predictions and results. Lawsuits and liability issues could follow on top of any damage done, so teams need to make sure the data they’re gathering is error-free.

Knowledge Demands 

Companies may need to hire personnel with the skills and knowledge needed to make use of digital twins. Even then, not all businesses have the budget to add these roles, and digital twins remain a difficult technology to understand and apply properly.

“The talent gap in areas like data science, AI and IoT is a barrier to digital twin adoption,” Schor said. “A lack of skilled professionals means that organizations might not be able to implement digital twin solutions, or they’ll be forced to rely on external vendors or partners to do so — thus leading to extra costs.”

Practical Limitations

A company’s existing tools and processes may not be compatible with digital twins, requiring teams to upgrade their tech stacks and digital infrastructure. This move may be unfeasible for businesses with limited resources. 

“Managing the sheer volume and variety of data required, at the desired quality, to power a digital twin is in many cases the foremost challenge in scaling digital twin technology,” Schor said. “A digital twin that is not scalable may become obsolete quickly, requiring costly upgrades, replacements or retirement.”

Ethical Issues 

The use of digital twins could lead to ethical dilemmas, especially in fields like healthcare. Besides questions around data privacy, there are fears that institutions could force or trick patients into letting them collect personal health data to build digital twins. Another concern is that the use of personalized digital twins may emphasize individual health factors and ignore the impact of broader social conditions.

 

How Does a Digital Twin Work?

Digital twin technology bring together a variety of disciplines, including 3D modeling, artificial intelligence and the Internet of Things:

1. 3D Modeling Software

A 3D model of an object must be made using 3D modeling tools and computer software. This model serves as the backbone of a digital twin, which supplements it with sensor data to develop a more complete digital copy.

2. IoT Sensor Data

Sensors are attached to the object and compile information about how it responds to a number of factors and stressors. Based on this real-time performance data, the digital twin can craft a detailed profile of the object that is dynamic, meaning any changes the object undergoes are also reflected in the digital twin.

This offers “the real value of the digital twin,” Dale Tutt, vice president of industry strategy at Siemens, told Built In. “It’s being able to use this digital twin throughout the entire product life cycle that gives it the most value.”

3. Artificial Intelligence

AI algorithms analyze the data transferred from IoT sensors, revealing patterns and delivering valuable insights. These algorithms are what drive the predictions of how objects may react under numerous circumstances. Teams can then act on these findings to fine-tune equipment, optimize processes, anticipate issues and address other business needs.

 

Digital Twin vs. Simulation

While both a digital twin and a simulation are used to reproduce and study the behavior of real-world objects, there are key differences between the two. 

A digital twin:

• Is a virtual representation of an object that leverages real-time data from sensors. 
• Delivers valuable insights on variables like an object’s current status, potential issues and historical performance rather than merely mimicking an object’s behavior. 
• Is a virtual environment that allows teams to run many simulations simultaneously to test all aspects of an object. 
• Creates a two-way flow of information, meaning users can indirectly control the object by manipulating its digital twin.

Meanwhile, a simulation:

• Uses algorithms and computer models to predict the behavior of an object under certain conditions. 
• Can only study one process or aspect of the object at a time. 
• Isn’t always updated with real-time data and doesn’t have a direct connection to the physical object. 
• Simply explores what-if scenarios by modeling how an object could behave using a predefined data set. 

Simulations can be seen as a key aspect of digital twins, but digital twins are much more comprehensive. As a result, equating the two is a major mistake yet a “common misconception” that businesses make, according to Tutt.

“[A digital twin is] all the information that you need to define the product digitally or the manufacturing process digitally, and really be able to then use that to make predictions, and to be able to optimize your processes,” Tutt said. “It’s more than 3D geometry, it’s more than simulations.”

 

History of Digital Twin Technology

Back in the 1990s, when Michael Grieves (now the executive director of the Digital Twin Institute) was a doctoral student, he didn’t think a paper about strategies for moving physical work into virtual spaces would make it past the dissertation committee. So he wrote a dissertation on business communications instead. But that didn’t stop him from considering new ways to meld the physical world with the digital.

At the time, engineers and manufacturers were taking the first steps from physical models and 2D blueprints into 3D computer-aided design (CAD) models. Meanwhile, computer processing power was poised to grow exponentially. Grieves, who’d worked on the first supercomputer, ILLIAC IV, suspected that, if those 3D models could take in more information, they’d provide unexpected value. Namely, work that used to only happen in the physical world could move into the virtual one, and possibilities that used to stay trapped in our heads could play out in digital spaces.

“There was this idea that I could create the product virtually, test it virtually, manufacture it virtually and support it virtually, and only when I got it all right did I have to go out and move around expensive atoms,” Grieves said.

When Grieves debuted his vision for the first time at a Society of Manufacturing Engineers conference in 2002, he presented it under the umbrella of “product lifecycle management.” It wasn’t until seven years later, when Grieves’ colleague, NASA Principal Technologist John Vickers, included the concept in an organizational roadmap report, that it received a snappy name — digital twin.

Consulting at NASA helped Grieves crystallize the value proposition for digital twins, he said: If the products you make get flung far into space, you can’t rely on physical proximity to understand and improve them. Add to that the costliness of aerospace manufacturing, and there’s good reason to do as much work as possible in the digital realm.

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